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Generalized linear mixed models a pseudo-likelihood approach
Abstract
A useful extension of the generalized linear model involves the addition of random effects andlor correlated errors. A pseudo-likelihood estimation procedure is developed to fit this class of mixed models based on an approximate marginal model for the mean response. The procedure is implemented via iterated fitting of a weighted Gaussian linear mixed model to a modified dependent variable. The approach allows for flexible specification of covariance structures for both the random effects and the correlated errors. An estimate of an additional dispersion parameter for underlying exponential family distributions is optionally automatic. The method allows for subject-specific and population-averaged inference, and the Salamander data example from McCullagh and Nelder (1989) is used to illustrate both.
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Citations
Cites
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“The full trend model was fitted by the residual penalized quasi-likelihood (pseudo-likelihood) method [84] in the SAS GLIMMIX procedure [80].”
Method
Metadata
- Authors
- Wolfinger, R., & O'connell, M.
- Venue
- Journal of Statistical Computation and Simulation
- Publisher
- Informa UK Limited
- Published
- 1993-12
- Volume
- 48
- Issue
- 3-4
- Pages
- 233-243
- ISSN
- 0094-9655 (ISSN-L)
- 1563-5163
- OpenAlex
- W2142045810
- Cited by
- 1,271
- References
- 2
- FWCI
- 6.02
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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